




"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''swinv2'''

   A_				      =       {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }







   def __init__(		self       ,					lowerCamelCase_=2_2_4       ,					lowerCamelCase_=4       ,					lowerCamelCase_=3       ,					lowerCamelCase_=9_6       ,					lowerCamelCase_=[2, 2, 6, 2]       ,					lowerCamelCase_=[3, 6, 1_2, 2_4]       ,					lowerCamelCase_=7       ,					lowerCamelCase_=4.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=False       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=1e-5       ,					lowerCamelCase_=3_2       ,					**lowerCamelCase_       ,					)				->   Any:
       super().__init__(**lowerCamelCase_)

       UpperCamelCase              =	image_size
       UpperCamelCase              =	patch_size
       UpperCamelCase              =	num_channels
       UpperCamelCase              =	embed_dim
       UpperCamelCase              =	depths
       UpperCamelCase              =	len(lowerCamelCase_)
       UpperCamelCase              =	num_heads
       UpperCamelCase              =	window_size
       UpperCamelCase              =	mlp_ratio
       UpperCamelCase              =	qkv_bias
       UpperCamelCase              =	hidden_dropout_prob
       UpperCamelCase              =	attention_probs_dropout_prob
       UpperCamelCase              =	drop_path_rate
       UpperCamelCase              =	hidden_act
       UpperCamelCase              =	use_absolute_embeddings
       UpperCamelCase              =	layer_norm_eps
       UpperCamelCase              =	initializer_range
       UpperCamelCase              =	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       UpperCamelCase              =	int(embed_dim * 2 ** (len(lowerCamelCase_) - 1))
       UpperCamelCase              =	(0, 0, 0, 0)






'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



